Artificial Intelligence & Generative AI
Speakers who decode the real-world impact of machine intelligence on industries, workforces and competitive advantage
Financial firms are under pressure to put generative and agentic AI into regulated work without breaching rules, losing trust, or building tools advisers ignore. Most boards can describe the opportunity; far fewer can describe the operating model, the controls, or where an agent stops helping and becomes a liability. The gap between AI ambition and deployment that creates value without eroding the business model is where most programmes stall.
Most large organisations have more knowledge than they can use and less curiosity than they need. Process discipline, accumulated expertise and AI tooling do not by themselves produce the next product, the next category, or the next reason for a customer to choose. Leaders are being asked to defend creative capacity inside companies that have spent two decades engineering it out.
Most boards now own an AI strategy on paper. Far fewer can defend, in front of customers, regulators or their own workforce, the design choices behind it. The gap between deploying AI and deploying it in a way that earns trust, holds up to scrutiny, and actually augments the people using it is where serious organisations are getting stuck.
Large organisations want the speed and originality of a founder-led startup, but the operating system inside them rewards the opposite behaviours. Boards approve innovation budgets and then watch promising pilots stall in legal, brand and procurement reviews. The harder question is how to design a venture inside a corporate parent so that it survives long enough to learn something useful.
Customer expectations now move faster than most innovation pipelines can absorb. Strategy teams see the shifts in the data, but by the time a proposition reaches market, the reference point has moved again. The real question is not which trend to chase, but how to build a repeatable method for turning early signals into commercial bets that leaders will back.
Most organisations have run AI pilots. Very few have converted them into operating performance. The gap is no longer about technical capability; it is about strategy, governance, sourcing decisions, and the readiness of the people who have to use the systems every day.
Most consumer businesses do not invent new categories, they iterate inside existing ones. The leaders who do invent categories then face a second problem: holding the category open against well-resourced incumbents while the underlying economics shift beneath them. Knowing how someone has actually run that loop, not theorised it, is what boards want when their own model is under strain.
Most large brands are running metaverse and avatar projects inside the same marketing teams that built their websites. The output is decorative, not commercial. Companies that want a serious return from digital worlds need to decide whether to retrofit existing functions or stand up a dedicated avatar-native business, and they need a credible view on which categories of revenue, audience, and intellectual property warrant the second route.
Digital transformation programmes still stall in the gap between the boardroom slide and the operating reality. Most leadership teams have the strategy. Few have run the messy work of converting telecoms, media and SaaS businesses from old revenue models into new ones, through acquisitions, restructurings and capital constraints. That is where the value is now decided.
Boards are being asked to make ten-year commitments on technologies that change every six months. Most leadership teams lack a decision architecture for this: they either freeze, or they pilot endlessly without operational deployment. The unresolved question is how to commit capital and reorganise work around AI without betting the firm on a single forecast.
Most boards now have an AI position on paper. Very few have a confident view of what their organisation should actually do with the technology, on what timeline, and at what cost to existing structures. The gap between AI as a slide in the strategy deck and AI as a real operating capability is where senior teams quietly stall.
Most financial crime training works off case studies written after the fact. It teaches people what fraud looks like from the outside. What it rarely gives them is the working logic of the person on the other side of the transaction. That blind spot is what allows sophisticated scams, mule-account networks and AI-enabled impersonation to keep finding room inside well-resourced institutions.